Non-contact method for detectiing and distinguishing human and animal based on ir-uwb bio-radar signal

ABSTRACT

A non-contact method for detecting and distinguishing a human and an animal based on IR-UWB bio-radar signals includes: step 1, transmitting a radar pulse to a target by a transmitting antenna of an IR-UWB bio-radar; and obtaining, by a receiving antenna of the IR-UWB bio-radar, a radar echo signal E(n,m) generated from the radar pulse reflected on the target; step 2: performing signal preprocessing on the radar echo signal E(n,m) obtained in step 1, to obtain a first energy signal E6(l); step 3: removing a direct wave from E6(l) obtained in step 2 to obtain a second energy signal E7(l), and obtaining a maximum amplitude E7max of E7(l) and a position lmax corresponding to the maximum amplitude in a slow time direction; step 4: calculating a peak-to-background ratio VEtoB of E7(l), calculating an average correlation coefficient rm, and determining a type of the target through a target detection and distinction rule.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to Chinese Patent ApplicationNo. 201911017732.0, filed on Oct. 24, 2019, the content of which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure belongs to the field of bio-radar technologies,and specifically, to a non-contact method for detecting anddistinguishing a human and an animal based on IR-UWB bio-radar signals.

BACKGROUND

Bio-radar is a technology which is capable of penetrating a certainmedium to achieve functions such as life body detection, vital signmonitoring, life body imaging and positioning in a non-contact andlong-distance manner, by extracting vital signs-related signals fromradar echoes. A principle of bio-radar is that radar emitselectromagnetic waves to a human body; the electromagnetic waves arereflected back to a radar receiving antenna after being modulated byhuman physiological activities such as breathing, heartbeat, and bodymovement; and after the radar receives the radar echo, physiological andbiological information about a human target is obtained from the radarecho through a certain signal processing technology. These informationincludes physiological parameters, waveforms, images, target positions,etc. Because of having the above advantages, the bio-radar technologyhas shown great advantages and broad application prospects in fields ofpost-disaster rescue, medical monitoring, anti-terrorism and stabilitymaintenance, and battlefield search and rescue.

At present, the detection research on bio-radar at home and abroad paysmore attention to whether human targets is in the detection results, aswell as other information such as the number, position, posture andcontour of targets. However, actual problems encountered in search andrescue operations after disasters such as earthquakes are complex anddiverse. For example, in the Wenchuan earthquake of 2008, a rescue teamused a bio-search and rescue radar to detect a life signal under arubble. After several hours of demolition and excavation by search andrescue officers and soldiers, it was found that a poultry was buriedunder the rubble. Therefore, in applications such as searching formissing persons in post-disaster rescue, it is not only desired to knowwhether there is a living body, but also whether the detected livingbody is a human or an animal. The accurate distinction and distinctionof humans and animals is of great practical significance forscientifically formulating rescue plans, saving limited rescue forcesand resources, improving an efficiency of search and rescue during agolden rescue period, enhancing search and rescue confidence ofrescuers, and more accurately identifying and saving lives of survivors.

When transmitted signals of the UWB bio-radar are irradiated to astationary object, echo signals of the radar are a stable fixed value.When transmitted signals of the UWB bio-radar are irradiated to lifebodies such as humans or animals, raw radar echo signals are cause tofluctuate due to slight fluctuations of a body surface caused bybreathing of the living body. In this way, life bodies can be detectedthrough these tiny fluctuations, and differences in characteristics ofslight fluctuations (characteristics such as energy amplitude, signalregularity, etc.) between humans and animals in the radar echo can beused to distinguish and discriminate between humans and animals.

SUMMARY

An object of the present disclosure is to provide a non-contact methodfor detecting and distinguishing a human and an animal based on IR-UWBbio-radar signals, to solve a problem of distinguishing anddiscriminating between the human and the animal.

In order to achieve above tasks, the present disclosure adopts followingtechnical solutions.

A non-contact method for detecting and distinguishing a human and ananimal based on IR-UWB bio-radar signals includes:

step 1: transmitting a radar pulse to a target by a transmitting antennaof an IR-UWB bio-radar, obtaining a radar echo signal E(n,m) through areceiving antenna of the IR-UWB bio-radar, where the radar echo signalis generated from the radar pulse reflected on the target, m is asampling ordinal number in a fast time direction, n is a samplingordinal number in a slow time direction, and m and n are positiveintegers;

step 2: performing signal preprocessing on the radar echo signal E(n,m)obtained in the step 1, to obtain a first energy signal E₆(l);

step 3: removing a direct wave from the first energy signal E₆(l)obtained in the step 2 to obtain a second energy signal E₇(l), obtaininga maximum amplitude value E_(7max) of the second energy signal E₇(l) anda position I_(max) corresponding to the maximum value in the slow timedirection;

step 4: calculating a peak-to-background ratio V_(EtoB) of the secondenergy signal E₇(l), and calculating an average correlation coefficientr_(m), and determining a type of the target through a target detectionand distinction rule, where the target detection and distinction ruleincludes:

a) if V_(EtoB)<σ_(N), a determination result is no target;

b) if σ_(N)≤V_(EtoB)<σ_(Y) and r_(m)<σ_(rm1) the determination result isno target;

c) if V_(EtoB)≥σ_(Y), the determination result is a human target;

d) if S_(thres)≤V_(EtoB)<σ_(Y) and r_(m)>σ_(rm2), the determinationresult is a human target; and

f) in cases other than a), b), c) and d), the determination result is ananimal target,

where σ_(N) represents a no target threshold; σ_(Y) represents a humantarget threshold; S_(thres) represents a sensitivity threshold andσ_(N)<S_(thres)<σ_(Y); σ_(N), σ_(Y), and S_(thres) are larger than 1;σ_(rm1) represent a weak correlation threshold; σ_(rm2) represents astrong correlation threshold and σ_(rm1)<σ_(rm2); σ_(rm1) and σ_(rm2)are both larger than 0 and smaller than 1.

Further, in the step 4, σ_(N)=1.65, σ_(Y)=8, σ_(rm1)=0.45, σ_(rm2)=0.92,S_(thres)={2, 3, 3.8}.

Further, the signal preprocessing in the step 2 includes followingsub-steps:

step 2.1: performing a distance accumulation on the radar echo signalE(n,m);

step 2.2: multiplying a signal after the distance accumulation in thestep 2.1 by an exponential gain curve G(l) of a formula I, to performattenuation compensation,

$\begin{matrix}{{G(l)} = {\exp\left( {\frac{l{g\left( V_{h} \right)}}{P} \times l} \right)}} & {{Formula}\mspace{14mu} I}\end{matrix}$

where V_(h) represents a ratio of the maximum value of the radar echodata to an amplitude of a target reflection echo, P represents a targetposition in units of m, l represents a fast time ordinal number afterthe distance accumulation, l=1, 2, . . . , L, and L is a positiveinteger;

step 2.3: removing a static clutter from the signal after theattenuation compensation in the step 2.2;

step 2.4: performing a linear trend subtraction from the signal afterthe static clutter is removed in the step 2.3;

step 2.5: performing, in the slow time dimension, low-pass filtering onthe signal after the linear trend subtraction in the step 2.4;

step 2.6: accumulating, along a slow time axis, the signal after thelow-pass filtering in the step 2.5, to obtain the first energy signalE₆(l).

Further, the average correlation coefficient r_(m) at a position of themaximum amplitude is calculated and obtained by a formula II:

$\quad\begin{matrix}\left\{ \begin{matrix}{{r_{i} = \frac{\begin{matrix}{\overset{Q}{\sum\limits_{q = 1}}\left( {{E_{({\max + {({i - 4})}})}(q)} - \overset{\_}{E_{({\max + {({i - 4})}})}(q)}} \right)} \\\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)\end{matrix}}{\begin{matrix}\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{({\max + {({i - 4})}})}(q)} - \overset{\_}{E_{({\max + {({i - 4})}})}(q)}} \right)^{2}} \\\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)^{2}}\end{matrix}}},\ {i \leq 3}} \\{{r_{i} = \frac{\begin{matrix}{\overset{Q}{\sum\limits_{q = 1}}\left( {{E_{({\max + {({i - 3})}})}(q)} - \overset{\_}{E_{({\max + {({i - 3})}})}(q)}} \right)} \\\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)\end{matrix}}{\begin{matrix}\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{({\max + {({i - 3})}})}(q)} - \overset{\_}{E_{({\max + {({i - 3})}})}(q)}} \right)^{2}} \\\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)^{2}}\end{matrix}}},\ {i \geq 4}} \\{r_{m} = {\frac{1}{6}{\sum\limits_{\;^{i = 1}}^{6}r_{i}}}}\end{matrix} \right. & {{Formula}\mspace{14mu}{II}}\end{matrix}$

where i represents an ordinal number of correlation coefficient and i=1,2, 3, 4, 5, 6, E₅(l,q) represents a signal of a middle channel after thelow-pass filtering in the slow time dimension obtained in the step 2.5,E_(max)(q) represents a signal at a position of the maximum of E₅(l,q),E_((max+(i−4)))(q) represents signals at the first three positionsadjacent to the position of the maximum of E₅(l,q), E_((max+(i−3)))(q)represents signals at the last three positions adjacent to the positionof the maximum value of E₅(l,q), Q represents the total number ofsampling points of the signal of E₅(l,q) in the slow time direction andQ is a positive integer, and q represents the q-th signal sampling pointin the slow time direction and q is a positive integer.

Compared with the related art, the present disclosure has followingtechnical characteristics.

1. In the disclosure, according to signal characteristics of human andanimal breathing, a set of target detection and distinction proceduresthat combine two characteristic parameters of energy-to-noise ratio andthe correlation coefficient average, and multiple thresholds, is mainlyadopted to realize detection and distinction between a human and ananimal, and to give a distance to the target.

2. In the present disclosure, the problem of energy attenuation withincreasing the distance in the transmission process of radar signals issolved, by pre-determining the target position and by performingattenuation compensation on the signal in the distance upwards accordingto the calculated exponential gain curve G(l). In this way, amiss-detection rate of a target in the distance is effectively reduced.In addition, compared with the segmental linear gain compensationmethod, the gain compensation adopted by the present disclosure is moreaccurate.

3. Physical significances, formulas, and calculation methods of the twocharacteristic parameters of the energy-to-noise ratio and thecorrelation coefficient average is provided according to the presentdisclosure, to give an optimal distinction threshold of each parameteron this basis. Under this optimal threshold condition, the detection anddistinction method proposed by the present disclosure can distinguish ahuman from animals such as dogs, cats, and poultry with a highrecognition accuracy rate.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a principle of an IR-UWB bio-radar system;

FIG. 2 is a schematic diagram of a two-dimensional matrix of radar echosignals;

FIG. 3 (a) shows a waveform of fast time signals;

FIG. 3 (b) shows a waveform of slow time signals;

FIG. 4 is a schematic diagram of IR-UWB radar detecting human breathing;

FIG. 5 is a schematic diagram of a pulse echo of human breathing;

FIG. 6 is a flowchart of a signal preprocessing algorithm;

FIG. 7 is a schematic diagram of simulated echo signals of a radar;

FIG. 8 is a schematic diagram of simulated echo signals after staticclutter is removed;

FIG. 9 is a flow chart of a method for detecting and distinguishing ahuman and an animal;

FIG. 10 is a schematic diagram of an energy signal E₆(l) according to afirst embodiment;

FIG. 11 is a schematic diagram of a slow time signal E_(max)(n) at aposition of a maximum value according to a first embodiment 1;

FIG. 12 is a schematic diagram of an energy signal E₆(l) according to asecond embodiment;

FIG. 13 is a schematic diagram of a slow time signal E_(max)(n) at aposition of a maximum value according to a second embodiment;

FIG. 14 is a schematic diagram of an energy signal E₆(l) according to athird embodiment;

FIG. 15 is a schematic diagram of a slow time signal E_(max)(n) at aposition of a maximum value according to the third embodiment;

FIG. 16 is a schematic diagram of an energy signal E₆(l) according to afourth embodiment; and

FIG. 17 is a schematic diagram of a slow time signal E_(max)(n) at aposition of a maximum value according to a fourth embodiment.

DESCRIPTION OF EMBODIMENTS

First, technical terms appearing in the present disclosure areexplained.

Bio-radar technology includes a continuous wave (CW) radar and anultra-wideband (UWB) radar, in which an ultra-wideband bio-radar becomesthe mainstream of current bio-radar technology research due to its highdistance-resolution and target recognition ability. An impulse-radioUltra-wideband (IR-UWB) radar has become a research hotspot inpost-disaster search and rescue fields due to its excellent performanceand simple structure. Therefore, identification and distinction betweena human and an animal is realized by an IR-UWB bio-radar system.

Slow time is a detection time of the radar on a target, in units ofsecond (s).

Fast time is a propagation time of a pulse, in units of nanosecond (ns).

As shown in FIG. 3, in actual detection, an echo signal sampled by theIR-UWB radar is sampled, integrated and amplified, and then is stored ina two-dimensional matrix R(m,n), where m is a row vector, n is a columnvector. In FIG. 3, a horizontal axis represents the slow time, and avertical axis represents the fast time. The fast time can be convertedinto a detection distance according to a propagation speed of anelectromagnetic wave in a medium, in units of meter (m).

A calculation relationship between the fast time and the distance is:distance (m)=fast time (ns)×propagation speed of electromagnetic wave inmedium (m/ns)/2.

Fast time signal is a signal at a certain moment, along a fast timedimension, that is, the column vector of the two-dimensional matrix.

Slow time signal is a signal at a certain distance point, along a slowtime dimension, that is, the row vector of the two-dimensional matrix.

A principle of the present disclosure is that when a signal transmittedby the bio-radar irradiates a stationary object, a radar echo signal isa stable fixed value; but when it irradiates living bodies such ashumans or animals, fluctuations are caused to appear in a raw radar echosignal, due to slight fluctuations of a body surface caused by breathingof the living body or the like. Thus, the living bodies can be detectedthrough these slight fluctuations.

An IR-UWB bio-radar system is provided according to the presentdisclosure. A structural block diagram of the system is as shown inFIG. 1. A pulse generator generates pulse signals with a certain pulserepeat frequency (PRF). The generated pulse signal is divided into twopaths: one path of the pulse signal is formed into a bipolar pulsesignal through an adjustment of a transmitting circuit and radiated outthrough a transmitting antenna; the other path of the pulse signal issent to a delay unit, to generate a series of range gates having anadjustable delay time under the control of a microprocessor. The rangegate is actually a sampling pulse signal having a very short duration.Under trigger of this signal, a receiving circuit can selectivelyreceive and sample the radar echo. The signal radiated by thetransmitting antenna is reflected when it encounters an object. Areflected radar echo is received by a receiving antenna; sent to thereceiving circuit to be selectively sampled, integrated, and amplifiedunder the trigger of the range gate; and then forms a radar echo signalthrough an Analog to Digital Converter (ADC). The radar echo signal issent, under the control of the microprocessor, to a processing anddisplay terminal via a WiFi module, for signal processing and resultdisplay.

FIGS. 3(a) and (b) show waveforms of the fast time signal and the slowtime signal of the radar echo, respectively. A width of a time window ofthe IR-UWB radar determines a length of the fast time signal. In anexperimental setup of the present disclosure, the width of the timewindow of one fast time signal is set to 80 ns, which corresponds to adetection distance in a range of 12 m. Each fast time signal consists of8192 sampling points, and a time interval between every two fast timesignals is T_(s)=0.0625 s. In other words, a sampling frequency of theslow time signal is f_(s)=1/T_(s)=16 Hz, which satisfies requirements ofNyquist sampling law for human breathing signal sampling.

In this embodiment, a non-contact method for detecting anddistinguishing a human and an animal based on IR-UWB bio-radar signalsis disclosed. The non-contact method includes following steps.

In step 1, the transmitting antenna of the IR-UWB bio-radar transmits aradar pulse to a target, and the radar pulse is reflected by the target,a radar echo signal E(n,m) is obtained through the receiving antenna ofthe IR-UWB bio-radar, where m is a sampling ordinal number in the fasttime direction, n is a sampling ordinal number in the slow timedirection, and m and n are positive integers.

In step 2, signal preprocessing is performed on the radar echo signalE(n,m) obtained in step 1, to obtain an energy signal E₆(l).

In step 3, a direct wave of the E₆(l) obtained in step 2 is removed toobtain an energy signal E₇(l), a maximum amplitude value E_(7max) ofE₇(l) and a position corresponding to the maximum amplitude value in theslow time direction are obtained, a distance from the target to theradar is obtained according to l_(max); a total number of samplingpoints corresponds to a total distance, a proportion of the samplingpoints corresponding to l_(max) in the total sampling points correspondsto a proportion of the distance from the target to the radar in thetotal distance, so as to obtain the distance from the target to theradar.

After a series of signal processing above, an amplitude of the energysignal is closely related to a life signal of a living body. The largerthe amplitude, the stronger the micro-motion signal of a life at thisdistance, the more likely it is a human or animal target;

In step 4, a peak-to-background ratio V_(EtoB) of E₇(l) and an averagecorrelation coefficient r_(m) are calculated, and a type of the targetis determined through a target detection and distinction rule, and thetarget detection and distinction rule includes:

a) if V_(EtoB)<σ_(N), a determination result is no target;

b) if σ_(N)≤V_(EtoB)<σ_(Y) and r_(m)<σ_(rm1) the determination result isno target;

c) if V_(EtoB)≥σ_(Y), the determination result is a human target;

d) if S_(thres)≤V_(EtoB)<σ_(Y) and r_(m)>σ_(rm2), the determinationresult is a human target; and

f) in cases other than a), b), c) and d), the determination result is ananimal target,

where σ_(N) represents a no target threshold; σ_(Y) represents a humantarget threshold; S_(thres) represents a sensitivity threshold andσ_(N)<S_(thres)<σ_(Y); σ_(N), σ_(Y), and S_(thres) are larger than 1;σ_(rm1) represent a weak correlation threshold; σ_(rm2) represents astrong correlation threshold and σ_(rm1)<σ_(rm2); σ_(rm1) and σ_(rm2)are both larger than 0 and smaller than 1.

In an embodiment, in step 4, σ_(N)=1.65, σ_(Y)=8, σ_(rm1)=0.45,σ_(rm2)=0.92, S_(thres) {2, 3, 3.8}. Three levels of the sensitivitythreshold S_(thres) are set. According to different detectionsensitivity requirements, a bio-radar operator can set three differentlevels of the sensitivity threshold. When the sensitivity thresholdS_(thres)=2, the bio-radar has the highest detection sensitivity, andthe bio-radar has the lowest detection sensitivity when S_(thres)=3.8.

The no target threshold and the human target threshold are obtained by aformula I:

$\begin{matrix}{\sigma_{V_{EtoB}} = {{a \times \frac{\begin{matrix}{{area}\mspace{14mu}{of}\mspace{14mu}{chest}\mspace{14mu}{wall}\mspace{14mu}{of}\mspace{14mu}{target} \times {amplitude}} \\{{of}\mspace{14mu}{breathing}\mspace{14mu}{of}\mspace{14mu}{target}}\end{matrix}}{\begin{matrix}{{{energy}\mspace{14mu}{level}\mspace{14mu}{of}\mspace{14mu}{background}}\mspace{14mu}} \\{{signal}\left( {{with}\mspace{14mu}{noise}} \right)}\end{matrix}}} = {a \times \frac{S \times A}{BN}}}} & {{Formula}\mspace{14mu} I}\end{matrix}$

In the formula I, a is a constant coefficient. In case of no target, aratio of the maximum amplitude value of signal to the background noiseis about 1.65 or smaller than 1.65. That is, in this case, the maximumamplitude value of signal is more than 1 time of the background noise.Energy levels of the maximum amplitude value of signal and thebackground noise are basically the same, and the waveform of the signalpresents a shape close to the waveform of the background noise withoutobvious peaks. When the target is a human target, rise and fall of abody surface of a chest caused by human breathing movement cause thesignal amplitude to increase at the target distance. In this time, theratio of the maximum amplitude value of the signal to the backgroundnoise is more than 2 times (which is different according to chest wallarea, breathing amplitude, background signal energy level of thetarget). In some scenarios, the ratio can even reach above 8. In thiscase, the maximum amplitude value of the signal has an obviously largerenergy level than the background noise, and the waveform of the signalpresents a significant peak shape at the target distance.

The weak correlation threshold and the strong correlation threshold areobtained by formula II:

$\begin{matrix}{\sigma_{r} = {{b \times \frac{\begin{matrix}{{thickness}\mspace{14mu}{of}\mspace{14mu}{chest}\mspace{14mu}{wall}\mspace{14mu}{of}\mspace{14mu}{target} \times {regularity}} \\{{level}\mspace{14mu}{of}\mspace{14mu}{breathing}\mspace{14mu}{of}\mspace{14mu}{target}}\end{matrix}}{{level}\mspace{14mu}{of}\mspace{14mu}{noise}}} = {b \times \frac{L \times R}{N}}}} & {{Formula}\mspace{14mu}{II}}\end{matrix}$

In the formula II, b is a constant coefficient. The weak correlationthreshold and the strong correlation threshold are determined accordingto measured data of a certain amount of samples in an experiment. Whenthe two thresholds are respectively 0.45 and 0.92, a distinction amonghumans, animals and no targets has the best effect.

Specifically, a method for obtaining the radar echo signal in step 1 isas below.

A schematic diagram of the IR-UWB radar detecting human breathing is asshown in FIG. 4. It is assumed that an initial distance between asurface of a chest wall of a human target and the radar is do. Breathingof the human body will cause a chest cavity to expand and contractperiodically. In normal circumstances, a displacement of a wall of thechest cavity when the human body breathes is a sine function x(t) withrespect to the slow time. Thus, an actual distance d(t) between thesurface of the chest wall of the human target and the radar willperiodically change around do according to a breathing frequency fr ofthe human body:

d(t)=d ₀ +x(t)=d ₀ +Ar sin(2πf _(r) t)

where t represents the slow time, x(t) represents change in thedisplacement of the chest wall during human breathing, and Ar representsthe maximum amplitude of human breathing.

In the detection range, an environment is static, the human target alsostays still, and only chest wall movement is caused by breathing. Thus,impulse response h(t,τ) of the radar system will change with time, likebreathing movement:

${h\left( {t,\tau} \right)} = {{\sum\limits_{i}{\alpha_{i}{\delta\left( {\tau - \tau_{i}} \right)}}} + {\alpha_{v}{\delta\left( {\tau - {\tau_{v}(t)}} \right)}}}$

In the formula, t represents the slow time, T represents the fast time;

$\sum\limits_{i}{\alpha_{i}{\delta\left( {\tau - \tau_{i}} \right)}}$

represents a pulse echo component of a static background target, whereαi and τi are respectively an amplitude of the pulse echo of the i-thstatic target and a delay in the fast time dimension of the pulse echoof the i-th static target; α_(v)δ(τ−τ(t)) represents a pulse echocomponent of the breathing movement of the human target, where α_(v) isan amplitude of the pulse echo, τ_(v)(t) is a delay change of the pulseecho of the human target in the fast time dimension, and it can beexpressed as:

${{\tau_{v}(t)} = {\frac{d(t)}{c} = {\frac{d_{0} + {{Ar}\mspace{14mu}{\sin\left( {2\pi f_{r}t} \right)}}}{c} = {\tau_{0} + {\tau_{r}{\sin\left( {2\pi f_{r}t} \right)}}}}}},$

where c is a propagation speed of electromagnetic waves in vacuum, τ_(r)is a maximum delay of breathing movement in the fast time dimension, τ₀is a delay of radar waves between the surface of the chest wall of thehuman body and the radar (an initial distance), namely

$\frac{d_{0}}{c}.$

If the pulse distortion and other non-linear effects are ignored, theradar echo signal can be regarded as a convolution of the transmittedpulse of the radar and system impulse response. Then, withoutconsidering noise, the radar echo signal at a moment oft is:

${{E\left( {\tau,t} \right)} = {{{p(\tau)}*{h\left( {t,\tau} \right)}} = {{\sum\limits_{i}{\alpha_{i}{p\left( {\tau - \tau_{i}} \right)}}} + {\alpha_{v}{p\left( {\tau - {\tau_{v}(t)}} \right)}}}}}.$

In this formula, p(τ) is a transmission pulse of the radar, and “*”means a convolution operation.

To explain the signal model more clearly, a schematic diagram of thepulse echo of human breathing is as shown in FIG. 5. It can be seen fromFIG. 5 that the delay of the pulse echo of human breathing in the fasttime dimension changes with the slow time, while the delay of the echodelay of the static target is constant.

In actual detection, the IR-UWB radar system samples respective pointson each pulse waveform at discrete moments τ=mT_(f) (m=1, 2, . . . , M)in the fast time direction, while samples the pulse waveform once ateach discrete moment t=nT_(s) (n=1, 2, . . . , N) in the slow timedirection. The sampled echo signals are stored as a (M×N)two-dimensional array E, and elements in the array E are represented byE(n,m):

${{E\left( {n,m} \right)} = {{\sum\limits_{i}{\alpha_{i}{p\left( {{mT_{f}} - \tau_{i}} \right)}}} + {\alpha_{v}{p\left( {{mT_{f}} - {\tau_{v}\left( {nT_{s}} \right)}} \right)}}}},$

where the signal E(n,m) is a two-dimensional signal, m is a samplingordinal number in the fast time direction, and n is a sampling ordinalnumber in the slow time direction.

Specifically, the signal preprocessing in step 2 includes followingsub-steps.

In step 2.1, a distance accumulation is performed on E(n,m), taking amiddle channel as an example.

The IR-UWB radar system used in this study has 8192 sampling points anda time window of 80 ns. Thus, if raw radar echo data is directlyprocessed, an amount of computation is large and operation is slow,which is disadvantageous to real-time of detection and recognition. Intwo-dimensional raw echo data E(n,m) received by the IR-UWB radar,modulation methods of radar echoes at adjacent distance points in thefast time dimension are roughly the same and have a certain correlation.Thus, under a premise of not affecting useful information, the distanceaccumulation can be performed on the raw radar echo data E(n,m) in thefast time dimension:

$\begin{matrix}{{{E_{1}\left( {l,n} \right)} = {\frac{1}{Q}{\sum\limits_{m = {{Q{({l - 1})}} + 1}}^{Ql}{E\left( {m,n} \right)}}}},} & \;\end{matrix}$

where E₁(l,n) (1=1, 2, . . . L) is echo data after the distanceaccumulation; Q is a window width accumulated in the fast timedimension; L is the number of distance points in the fast time dimensionafter accumulation, and

${L = \left\lfloor \frac{M}{Q} \right\rfloor},$

where “└ ┘” means rounding down. A large number of experimental studiesshow that an algorithm achieves the best results when the window widthQ=40. After the distance accumulation, the slow time signals on the 8192corresponding distance points of the raw echo data E(m, n) are reducedto the fast time signals on the 200 (that is, L=200) correspondingdistance points of E₁(l,n), thereby reducing the amount of computationin the radar data processing process to a great extent, decreasingcomputation time required for detection, and improving a detectionefficiency. Moreover, the distance accumulation along the fast timedimension is also equivalent to smoothing and filtering the fast timesignals of the radar echo, which can suppress high frequencyinterference on the fast time signal to a certain extent.

In step 2.2, the signal after the distance accumulation in step 2.1 ismultiplied by an exponential gain curve G(l) of the formula III toperform attenuation compensation:

$\begin{matrix}{{G(l)} = {\exp\left( {\frac{l{g\left( V_{h} \right)}}{P} \times l} \right)}} & {{Formula}\mspace{14mu}{III}}\end{matrix}$

where V_(h) represents a ratio of the maximum of the radar echo data toan amplitude of the target reflection echo, P is a target position inunits of m, l represents a fast time ordinal number after the distanceaccumulation, l=1, 2, . . . , L, and L is a positive integer.

As the radar wave is severely attenuated in the medium propagationprocess, an amplitude of an echo reflected on an object interface in adistance will be greatly reduced. Thus, the object in the distance willdifficult to be detected. It is necessary to compensate the radar echoE₁(l,n) after the distance accumulation before identifying the echoreflected on an interface. Current ultra-wide spectrum radars (mainlyground penetrating radars) have automatic gain adjustment functions, toamplify the echo data reflected on the object in the distance throughsegmental linear or exponential gain adjustment on the radar echo.However, due to lack of prior knowledge of interface information ofelectromagnetic wave propagation medium, accuracy of gain calculationhas a poor accuracy. Noises may be over-amplified due to the inaccurategain, whereas the real echo reflected on the interface cannot beproperly amplified because of the relatively small gain, ultimatelyleading to a greatly increased probability of the target being misjudgedand miss-judged.

The method of segmental compensation for attenuation needs to calculatedifferent gains for possible attenuation of each segment of the echo,and the calculation process is too complicated. However, it is difficultto calculate the gain accurately, and it is easily affected by noise,leading to wrong compensation. In actual detection, a position of thehuman target can be detected and calculated at first without thecompensation, which is used as prior knowledge. Due to an exponentialattenuation of the electromagnetic waves during propagation in themedium, the position of the human target and the corresponding reflectedecho amplitude are taken as a compensation benchmark, and the method ofthe exponential gain compensation is adopted to perform attenuationcompensation for the radar echo data in the fast time dimension. Afterthe compensation, the signal is processed again according to the signalprocessing flow, and the target is detected and distinguished.

The calculation method of the gain curve is as follows.

It is assumed that the ideal exponential gain curve looks like e^(K×t),where K is an unknown constant. For the preprocessed data, the maximumA_(max) (usually the maximum of the radar echo data) of E₁(l,n) isdivided by an amplitude A_(human) of the reflection echo of the humantarget (that is, an amplitude of the radar echo data corresponding tothe human target position P_(human)), to obtain a ratio V_(h). Takingthis ratio V_(h) as an ideal gain value at the radar echo position ofP_(human), the exponential gain curve that changes with the fast timeordinal number l can be calculated. The calculated exponential gaincurve is multiplied with the radar echo data on the fast time axis, torealize the attenuation compensation of the radar echo data. The signalafter the attenuation compensation is E₂(l,n), and E₂(l,n)=G(l)E₁(l,n).

In step 2.3, static clutter is removed from the signal after theattenuation compensation in Step 2.2.

In the radar-based life detection process, a direct wave of the radarand a reflection of stationary objects within the detection range willform strong background clutters in the radar echo signal. The breathingsignal of the human target is very weak, which is easy to be submergedby these background clutters. As shown in FIG. 3, in the raw radar echo,the life signal of the human target can hardly be seen, and only thebackground clutters can be seen. In an ideal condition, these backgroundclutters are static, called static clutter, while only the life signalof the human target changes over time. Thus, the static clutter can becompletely filtered out by subtracting an average of the slow timesignal of the echo, leaving only the life signal of the human body:

${{E_{3}\left( {l,n} \right)} = {{E_{2}\left( {l,n} \right)}\frac{1}{N}{\sum\limits_{n = 1}^{N}{E_{2}\left( {l,n} \right)}}}},$

where E₃(l,n) is a radar echo signal after the background is removed.

FIG. 7 is a two-dimensional radar signal simulated by a matlab software.It can be seen from FIG. 7 that there are static clutters near 15 ns and65 ns, which do not change with the slow time, while the breathingsignal of the human target around 40 ns changes regularly along the slowtime dimension. After the average removing method is applied to thesimulated two-dimensional radar signal to eliminate the static clutters,a component of the static clutter in the echo is completely removed,leaving only the breathing signal of the human target, as shown in FIG.8.

In step 2.4, a linear trend subtraction is performed from the signalafter the static clutter is removed in step 2.3.

The hardware of the IR-UWB radar system is often accompanied by abaseline drift of the echo in the data acquisition process. The linearbaseline drift will cause energy leakage of the echo data in a lowfrequency band, thereby affecting the detection and recognition of thebreathing signal of the human target. Therefore, in the presentdisclosure, a linear trend subtraction (LTS) is used to remove thelinear baseline drift in the radar echo signal. LTS estimates, throughlinear least squares fitting, a direct-current component of the echosignal E₃(l,n) in the slow time dimension and a low-frequency lineardrift trend, which is subtracted from the echo data:

E ₄ ^(T) =E ₃ ^(T) −y(y ^(T) y)⁻¹ y ^(T) E ₃ ^(T),

In the formula, E₄ represents the radar data after the LTS processing,E₃ represents the radar data E₃(l,n) after removing the average; E₄ ^(T)and E₃ ^(T) are their transposed determinants respectively.

${y = \left\lbrack {\frac{n}{N},1_{N}} \right\rbrack},$

n=[0, 1, 2 . . . , N−1]^(T), here y is a determinant with N rows and 2columns, 1_(N) is a column vector having a length of N and having allelements being 1, and N is the number of the fast time signals in E₃.After the linear trend subtraction, E₄ ^(T) is transposed to getE₄(l,n).

In step 2.5, a low-pass filtering is performed on the signal after thelinear trend subtraction in Step 2.4, in the slow time dimension.

Since the hardware of the IR-UWB radar system will inevitably producenoise in a working process, these noises are high-frequency noisesrelative to the breathing signal of the human body. The breathing signalof the human target is a narrow-band low-frequency quasi-periodicsignal. Thus, in order to effectively filter out high-frequencyinterference and further improve a signal-to-noise ratio of the radarecho, a low-pass filtering is performed on the radar echo signal in theslow time dimension according to the present disclosure:

E ₅(l,q)=E ₄(l,n)*h(t)

where E₅(l,q) is the radar data after filtering, “*” represents aconvolution operation, h(t) is an impulse function of a Finite ImpulseResponse (FIR) filter. According to the breathing frequency of the humanbody, a cutoff frequency of the low-pass filter is set to 0.5 Hz, and anorder of the filter is 120. The radar echo signal after the low-passfiltering is E₅(l,q).

In step 2.6, the signal after the low-pass filtering in step 2.5 isaccumulated, along a slow time axis, to obtain an energy signal E₆(l).

In the experiment, the experimental data is collected for t_(s)=80seconds each time; and according to the sampling frequency f_(s)=16 Hzof the slow time signal, it can be known that the data collected eachtime contains 16×80=1280 fast time signals. That is, the radar echosignal after the low-pass filtering is Q=t_(s)f_(s)=1280 sampling datain the E₅(l,q). The value of L is obtained after the distanceaccumulation, which is 200 (200 is obtained from 8192 sampling points ofthe fast time signal through the distance accumulation, mainly to reducethe computation; this value can be determined freely, and thecomputation can be reduced once accumulating to 200-1000 points withoutaffecting the signal quality).

Because the steps of removing the average and low-pass filtering inpreprocessing require a convergence process, the first 200 (the 200 hereis related to a sum of the orders of removing the average and low-passfiltering in the preprocessing; the lower the order, the smaller thevalue can be, and the higher the order, the larger the value needs tobe) fast time signals are not used as a basis for detection andrecognition of the target and are eliminated. Absolute values of 1000(here 1000 is determined by a length of the sampling time, the samplingfrequency of 16 Hz corresponds to data sampled every about 62.5 seconds;and the longer the signal sample interval, the larger the value) fasttime signals (200-1200) in the E₅(l,q) is calculated, they areaccumulated along the slow time axis to form an energy signal E₆(l).

$\begin{matrix}{{E_{6}(l)} = {\sum\limits_{n = 200}^{1200}{{E_{5}\left( {l,q} \right)}}}} & \;\end{matrix}$

The energy signal E₆(l), (l=1, 2, . . . , 200) is a one-dimensionalsignal; an abscissa thereof is the fast time, corresponding to distance(m); and an ordinate thereof is an energy amplitude accumulated alongthe slow time. After a series of signal processing above, the amplitudeof the energy signal is closely related to the life signal of the lifebody. The larger the amplitude, indicating that the stronger the slightmovement signal of life at this distance, the more likely it is a humanbody or biological target.

Specifically, removing the direct wave in the step 3 includes:discarding data of the first 50 points in the E₆(l), and renumberingdata of the remaining 150 points as 1-150, to form a new energy signalE₇(l) in which the direct wave is removed.

Specifically, step 4 includes following sub-steps:

In step 4.1, an average of the data of the two sections 1˜(l_(max)−12)and l_(max)+25)˜150 of the energy signal obtained in the step 3 iscalculated, to obtain a background average B_(ave);

In step 4.2, a peak-to-background ratio V_(EtoB) is obtained bycalculating through a following formula:

${V_{EtoB} = \frac{E_{7\max}}{B_{ave}}}.$

In step 4.3, obtaining the slow time signal E_(max)(n) at the positionof the maximum value l_(max) of the energy signal is obtained accordingto E₅(l,n), correlation coefficients of six positions adjacent to theposition of the maximum value are calculating, and an average of sixcorrelation coefficients is calculated. The six positions include firstthree positions and last three positions adjacent to the position.

The slow time signals at positions of (l_(max)−3), (l_(max)−2),(l_(max)−1), (l_(max)+1), (l_(max)+2), (l_(max)+3) are obtained, whichare marked as E_(max−2)(q), E_(max−2) (q), E_(max−1)(q), E_(max+1)(q),E_(max+2)(q), E_(max+), (q) respectively. The correlation coefficientsbetween these six slow time signals and E_(max)(q) are calculated. Theaverage r_(m) of the correlation coefficients at the maximum value iscalculated and obtained by the formula IV:

$\begin{matrix}\left\{ \begin{matrix}{{r_{i} = \frac{\begin{matrix}{\underset{q = 1}{\sum\limits^{Q}}\left( {{E_{({\max + {({i - 4})}})}(q)} - \overset{\_}{E_{({\max + {({i - 4})}})}(q)}} \right)} \\\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)\end{matrix}}{\begin{matrix}\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{({\max + {({i - 4})}})}(q)} - \overset{\_}{E_{({\max + {({i - 4})}})}(q)}} \right)^{2}} \\\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)^{2}}\end{matrix}}},{i \leq 3}} \\{{r_{i} = \frac{\begin{matrix}{\underset{q = 1}{\sum\limits^{Q}}\left( {{E_{({\max + {({i - 3})}})}(q)} - \overset{\_}{E_{({\max + {({i - 3})}})}(q)}} \right)} \\\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)\end{matrix}}{\begin{matrix}\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{({\max + {({i - 3})}})}(q)} - \overset{\_}{E_{({\max + {({i - 3})}})}(q)}} \right)^{2}} \\\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)^{2}}\end{matrix}}},{i \geq 4}} \\{r_{m} = {\frac{1}{6}{\sum\limits_{i = 1}^{6}r_{i}}}}\end{matrix} \right. & {{Formula}\mspace{14mu}{IV}}\end{matrix}$

In formula IV, i represents an ordinal number of correlation coefficientand i=1, 2, 3, 4, 5, 6; E₅(l,q) represents the signal of the middlechannel after low-pass filtering in the slow time dimension, obtained instep 2.5; E_(max)(q) represents the signal at the position of themaximum value of E₅(l,q); E_(max+(i−4)))(q) represents the signals atthe first three positions adjacent to the position of the maximum valueof E₅(l,q); E_(max+(i−3)))(q) represents the signals at the last threepositions adjacent to the position of the maximum value of E₅(l,q); Qrepresents the total number of sampling points of the signal of E₅(l,q)in the slow time direction and Q is a positive integer; and q representsthe q-th signal sampling point in the slow time direction and q is apositive integer. The six correlation coefficients calculated accordingto the above formula are respectively marked as: r₁, r₂, r₃, r₄, r₅, r₆,and the average r_(m) of the six correlation coefficients at the maximumvalue of a secondary inflection point signal is calculated.

In step 4.4, establishing a target detection and distinction rule isestablished and the type of the target is obtained through the targetdetection and distinction rule.

Verification Experiment and Results

Through-wall detection and verification experiments are conducted in alaboratory by the method for detecting and distinguishing a human and ananimal and the IR-UWB bio-radar. In the experiment, a healthy male dogand two healthy young men (target A and target B) were selected fordetection experiments under a condition of penetrating a singlebrick-wall respectively, and distinguishing results are given. Theexperiment was conducted in 30 groups, which include 10 groups of notarget, 10 groups of animal target and 10 groups of single human target.In an experiment of single human target, the two experimental subjects Aand B are each detected by 5 groups, and a sensitivity threshold for alldata detection is set to 2. The detection and distinction results showthat a detection accuracy rate is 90% in the case of no target, and onegroup of data was misjudged as an animal target; the detection accuracyin the case of a single human target is 80%, and two groups of data aremisjudged as an animal due to a relatively small peak-to-backgroundratio, that is 1.65≤V_(EtoB)<8 and 0.45<r_(m)<0.92; the detectionaccuracy rate for the animal target is 80%, one group of data ismisjudged as no target because of V_(EtoB)<1.65, and one group ismisjudged as no target because of 1.65 V_(EtoB)<8 and r_(m)<0.45. Therecognition and distinction accuracy rates of the three groups ofexperiments all reached 80% or more, meeting the requirements ofdetection and distinction. Here are four experimental data with correctdetection results selected as follows:

Experiment 1

Experimental scene: through-wall detection for no target, and thesensitivity threshold is set to 2.

The peak-to-background ratio is V_(EtoB)=1.809, 1.65≤V_(EtoB)<2; in thiscase r_(m)=0.095, r_(m)<0.45. Then the determination result is notarget, and the determination result is correct.

Experiment 2

Experimental scene: through-wall detection for the animal target (dog),and the sensitivity threshold is set to 2.

The peak-background ratio is V_(EtoB)=3.309, 2≤V_(EtoB)<8; the averageof the correlation coefficients is r_(m)=0.707, 0.45≤r_(m)≤0.92. Thenthe determination result is an animal target, a distance is 3.32 metersbehind the wall, and the determination result is correct.

Experiment 3

Experimental scene: through-wall detection for the human target, and thesensitivity threshold is set to 2.

The peak-to-background ratio is V_(EtoB)=3.69, 2≤V_(EtoB)<8; the averageof the correlation coefficients is r_(m)=0.9257, r_(m)>0.92. Then, thedetermination result is a human target, the distance is 4.51 metersbehind the wall, and the determination result is correct.

Experiment 4

Experimental scene: through-wall detection of the human target, and thesensitivity threshold is set to 2.

The peak-to-background ratio is V_(EtoB)=11.04, V_(EtoB)>8. It isdirectly determined as a human target, the distance is 3.64 metersbehind the wall, and the determination result is correct.

What is claimed is:
 1. A non-contact method for detecting anddistinguishing a human and an animal based on Impulse-RadioUltra-Wideband (IR-UWB) bio-radar signals, comprising: step 1:transmitting a radar pulse to a target by a transmitting antenna of anIR-UWB bio-radar, obtaining a radar echo signal E(n,m) through areceiving antenna of the IR-UWB bio-radar, wherein the radar echo signalis generated from the radar pulse reflected on the target, m is asampling ordinal number in a fast time direction, n is a samplingordinal number in a slow time direction, and m and n are positiveintegers; step 2: performing signal preprocessing on the radar echosignal E(n,m) obtained in the step 1, to obtain a first energy signalE₆(l); step 3: removing a direct wave from the first energy signal E₆(l)obtained in the step 2 to obtain a second energy signal E₇(l), andobtaining a maximum amplitude E_(7max) of the second energy signal E₇(l)and a position l_(max) corresponding to the maximum amplitude in theslow time direction; and step 4: calculating a peak-to-background ratioV_(EtoB) of the second energy signal E₇(l), and calculating an averagecorrelation coefficient r_(m), and determining a type of the targetthrough a target detection and distinction rule, wherein the targetdetection and distinction rule comprises: a) if V_(EtoB)<σ_(N), adetermination result is no target; b) if σ_(N)≤V_(EtoB)<σ_(Y) andr_(m)<σ_(rm1), the determination result is no target; c) if V_(EtoB) thedetermination result is a human target; d) if S_(thres)≤V_(EtoB)<σ_(Y)and r_(m)>σ_(rm2), the determination result is a human target; and f) incases other than a), b), c), and d), the determination result is ananimal target; where σ_(N) represents a no target threshold; σ_(Y)represents a human target threshold; S_(thres) represents a sensitivitythreshold and σ_(N)<S_(thres)<σ_(Y); and σ_(N), σ_(Y), and S_(thres) arelarger than 1; σ_(rm1) represent a weak correlation threshold; σ_(rm2)represents a strong correlation threshold and σ_(rm1)<σ_(rm2); σ_(rm1)and σ_(rm2) are both larger than 0 and smaller than
 1. 2. Thenon-contact method for detecting and distinguishing a human and ananimal based on IR-UWB bio-radar signals according to claim 1, whereinin the step 4, σ_(N)=1.65, σ_(Y)=8, σ_(rm1)=0.45, 6_(rm2)=0.92, andS_(thres)={2, 3, 3.8}.
 3. The non-contact method for detecting anddistinguishing a human and an animal based on IR-UWB bio-radar signalsaccording to claim 1, wherein the signal preprocessing in the step 2comprises: step 2.1: performing a distance accumulation on the radarecho signal E(n,m); step 2.2: multiplying a signal after the distanceaccumulation in the step 2.1 by an exponential gain curve G(l) of aformula I, to perform attenuation compensation, $\begin{matrix}{{G(l)} = {\exp\left( {\frac{l{g\left( V_{h} \right)}}{P} \times l} \right)}} & {{Formula}\mspace{14mu} I}\end{matrix}$ where V_(h) represents a ratio of the maximum of the radarecho data to an amplitude of a target reflection echo, P represents atarget position in units of m, l represents a fast time ordinal numberafter the distance accumulation, l=1,2, . . . , L, and L is a positiveinteger; step 2.3: removing a static clutter from the signal after theattenuation compensation in the step 2.2; step 2.4: performing a lineartrend subtraction from the signal after the static clutter is removed inthe step 2.3; step 2.5: performing, in the slow time dimension, low-passfiltering on the signal after the linear trend subtraction in the step2.4; and step 2.6: accumulating, along a slow time axis, the signalafter the low-pass filtering in the step 2.5, to obtain the first energysignal E₆(l).
 4. The non-contact method for detecting and distinguishinga human and an animal based on IR-UWB bio-radar signals according toclaim 3, wherein the average correlation coefficient r_(m) at a positionof the maximum amplitude is calculated and obtained by a formula II:$\begin{matrix}\left\{ \begin{matrix}{{r_{i} = \frac{\begin{matrix}{\underset{q = 1}{\sum\limits^{Q}}\left( {{E_{({\max + {({i - 4})}})}(q)} - \overset{\_}{E_{({\max + {({i - 4})}})}(q)}} \right)} \\\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)\end{matrix}}{\begin{matrix}\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{({\max + {({i - 4})}})}(q)} - \overset{\_}{E_{({\max + {({i - 4})}})}(q)}} \right)^{2}} \\\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)^{2}}\end{matrix}}},{i \leq 3}} \\{{r_{i} = \frac{\begin{matrix}{\underset{q = 1}{\sum\limits^{Q}}\left( {{E_{({\max + {({i - 3})}})}(q)} - \overset{\_}{E_{({\max + {({i - 3})}})}(q)}} \right)} \\\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)\end{matrix}}{\begin{matrix}\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{({\max + {({i - 3})}})}(q)} - \overset{\_}{E_{({\max + {({i - 3})}})}(q)}} \right)^{2}} \\\sqrt{\sum\limits_{q = 1}^{Q}\left( {{E_{\max}(q)} - \overset{\_}{E_{\max}(q)}} \right)^{2}}\end{matrix}}},{i \geq 4}} \\{r_{m} = {\frac{1}{6}{\sum\limits_{i = 1}^{6}r_{i}}}}\end{matrix} \right. & {{Formula}\mspace{14mu}{II}}\end{matrix}$ where i represents an ordinal number of correlationcoefficient and i=1, 2, 3, 4, 5, 6; E₅(l,q) represents a signal of amiddle channel after the low-pass filtering in the slow time dimension,obtained in the step 2.5; E_(max)(q) represents a signal at a positionof the maximum of E₅(l,q); E_(max+(i−4)))(q) represents signals at thefirst three positions adjacent to the position of the maximum ofE₅(l,q); E_(max+(i−3)))(q) represents signals at the last threepositions adjacent to the position of the maximum of E₅(l,q); Qrepresents the total number of sampling points of the signal of E₅(l,q)in the slow time direction and Q is a positive integer; and q representsthe q-th signal sampling point in the slow time direction and q is apositive integer.